Incorporating a situational judgement test in residency selections: clinical, educational and organizational outcomes
Bibliographic record
Abstract
BACKGROUND: Computer-based assessment for sampling personal characteristics (Casper), an online situational judgement test, is a broad measure of personal and professional qualities. We examined the impact of Casper in the residency selection process on professionalism concerns, learning interventions and resource utilization at an institution. METHODS: In 2022, admissions data and information in the files of residents in difficulty (over three years pre- and post- Casper implementation) was used to determine the number of residents in difficulty, CanMEDS roles requiring a learning intervention, types of learning interventions (informal learning plans vs. formal remediation or probation), and impact on the utilization of institutional resource (costs and time). Professionalism concerns were mapped to the 4I domains of a professionalism framework, and their severity was considered in mild, moderate, and major categories. Descriptive statistics and between group comparisons were used for quantitative data. RESULTS: In the pre- and post- Casper cohorts the number of residents in difficulty (16 vs. 15) and the number of learning interventions (18 vs. 16) were similar. Professionalism concerns as an outcome measure decreased by 35% from 12/16 to 6/15 (p < 0.05), were reduced in all 4I domains (involvement, integrity, interaction, introspection) and in their severity. Formal learning interventions (15 vs. 5) and informal learning plans (3 vs. 11) were significantly different in the pre- and post-Casper cohorts respectively (p < 0.05). This reduction in formal learning interventions was associated with a 96% reduction in costs f(rom hundreds to tens of thousands of dollars and a reduction in time for learning interventions (from years to months). CONCLUSIONS: Justifiable from multiple stakeholder perspectives, use of an SJT (Casper) improves a clinical performance measure (professionalism concerns) and permits the institution to redirect its limited resources (cost savings and time) to enhance institutional endeavors and improve learner well-being and quality of programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".